Nuclear Modification to Parton Distribution Functions and Parton Saturation

We introduce a generalized definition of parton distribution functions (PDFs) for a more consistent all-order treatment of power corrections. We present a new set of modified DGLAP evolution equations for nuclear PDFs, and show that the resummed $\alpha_s A^{1/3}/Q^2$-type of leading nuclear size enhanced power corrections significantly slow down the growth of gluon density at small-$x$. We discuss the relation between the calculated power corrections and the saturation phenomena.Comment: 4 pages, to appear in the proceedings of QM200

Dynamical properties of a trapped dipolar Fermi gas at finite temperature

We investigate the dynamical properties of a trapped finite-temperature normal Fermi gas with dipole-dipole interaction. For the free expansion dynamics, we show that the expanded gas always becomes stretched along the direction of the dipole moment. In addition, we present the temperature and interaction dependences of the asymptotical aspect ratio. We further study the collapse dynamics of the system by suddenly increasing the dipolar interaction strength. We show that, in contrast to the anisotropic collapse of a dipolar Bose-Einstein condensate, a dipolar Fermi gas always collapses isotropically when the system becomes globally unstable. We also explore the interaction and temperature dependences for the frequencies of the low-lying collective excitations.Comment: 11 pages, 7 figure

Role of the nonperturbative input in QCD resummed Drell-Yan QTQ_T-distributions

We analyze the role of the nonperturbative input in the Collins, Soper, and Sterman (CSS)'s $b$-space QCD resummation formalism for Drell-Yan transverse momentum ($Q_T$) distributions, and investigate the predictive power of the CSS formalism. We find that the predictive power of the CSS formalism has a strong dependence on the collision energy $\sqrt{S}$ in addition to its well-known $Q^2$ dependence, and the $\sqrt{S}$ dependence improves the predictive power at collider energies. We show that a reliable extrapolation from perturbatively resummed $b$-space distributions to the nonperturbative large $b$ region is necessary to ensure the correct $Q_T$ distributions. By adding power corrections to the renormalization group equations in the CSS formalism, we derive a new extrapolation formalism. We demonstrate that at collider energies, the CSS resummation formalism plus our extrapolation has an excellent predictive power for $W$ and $Z$ production at all transverse momenta $Q_T\le Q$. We also show that the $b$-space resummed $Q_T$ distributions provide a good description of Drell-Yan data at fixed target energies.Comment: Latex, 43 pages including 15 figures; typos were correcte

Resummed QCD Power Corrections to Nuclear Shadowing

We calculate and resum a perturbative expansion of nuclear enhanced power corrections to the structure functions measured in deeply inelastic scattering of leptons on a nuclear target. Our results for the Bjorken $x$-, $Q^2$- and $A$-dependence of nuclear shadowing in $F_2^A(x,Q^2)$ and the nuclear modifications to $F_L^A(x,Q^2)$, obtained in terms of the QCD factorization approach, are consistent with the existing data. We demonstrate that the low-$Q^2$ behavior of these data and the measured large longitudinal structure function point to a critical role for the power corrections when compared to other theoretical approaches.Comment: 4 pages, 3 figures, uses RevTeX 4. As published in Phys.Rev.Let

Resummation of nuclear enhanced higher twist in the Drell Yan process

We investigate higher twist contributions to the transverse momentum broadening of Drell Yan pairs in proton nucleus collisions. We revisit the contribution of matrix elements of twist-4 and generalize this to matrix elements of arbitrary twist. An estimate of the maximal nuclear broadening effect is derived. A model for nuclear enhanced matrix elements of arbitrary twist allows us to give the result of a resummation of all twists in closed form. Subleading corrections to the maximal broadening are discussed qualitatively.Comment: 10 pages, 5 figures; v2: minor changes in text, acknowledgement added; v3: mistake in fig. 1 correcte

On several families of elliptic curves with arbitrary large Selmer groups

In this paper, we calculate the $\phi (\hat{\phi})-$Selmer groups S^{(\phi)} (E / \Q) and S^{(\hat{\varphi})} (E^{\prime} / \Q) of elliptic curves $y^{2} = x (x + \epsilon p D) (x + \epsilon q D)$ via descent theory (see [S, Chapter X]), in particular, we obtain that the Selmer groups of several families of such elliptic curves can be arbitrary large.Comment: 22 page

Nuclear Effects on Heavy Boson Production at RHIC and LHC

We predict W and Z transverse momentum distributions from proton-proton and nuclear collisions at RHIC and LHC. A resummation formalism with power corrections to the renormalization group equations is used. The dependence of the resummed QCD results on the non-perturbative input is very weak for the systems considered. Shadowing effects are discussed and found to be unimportant at RHIC, but important for LHC. We study the enhancement of power corrections due to multiple scattering in nuclear collisions and numerically illustrate the weak effects of the dependence on the nuclear mass.Comment: 21 pages, 11 figure

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201